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In an earlier piece I deliberately kept my conclusions cautious. We do not know how far the automation of knowledge work by AI will go, or how quickly. Let me set that caution aside for a moment: suppose it succeeds. Suppose that within the foreseeable future, machines take over a large share of paid intellectual work. What then? The first reaction is usually fear and unease — about money, but above all about the question of what people are still for. We have tied work, income and identity together so tightly that we can barely imagine a dignified life without paid employment. That is precisely where we need to start. ...

Imagine a seventeen-year-old choosing a course of study right now. She is weighing up law, something in administration, or a degree in design. She does what we have been advising her to do for years: pick a direction and invest seriously in an education, in money and in time. The adults around her quietly assume that by the time she graduates, an entry-level job will be waiting for her, and a career beyond it. ...

With AI we have realised something we regarded as uniquely human — our intelligence — inside a machine. And now we stand looking at it. What we see surprises and unsettles us: perhaps intelligence is not quite what we thought it was, and perhaps it is not ours alone. This kind of unsettling has happened before. Copernicus showed that humanity is not the centre of the universe; Darwin showed that humans are animals among other animals. Sigmund Freud described such shifts as “wounds” to human self-love — painful decentrings in which humanity must let go of deeply held beliefs. And that pain calls for grief. ...

In the article “More AI, less enrolment is a dangerous combination”, Anita Bosman and Klaas Brongers describe a worrying trend: young people are choosing not to study ICT because of AI. Meanwhile software is rapidly penetrating every corner of society, and the shortage of ICT professionals is structural. Their suggestion to make ICT education more attractive is well-taken. But it is easy to miss the mark. Adding a healthy dose of AI to the curriculum and explaining that ICT really does matter for society is not going to be enough. Because the underlying fear is left unaddressed: that AI makes programmers redundant. As long as that perception persists among students, parents and careers advisers, little will change. ...

Pieter processed address changes for a large insurance company. Predictable, straightforward work. Useful and important — for him and for his employer. Until customers could submit their own address changes online. After that, there was no suitable work left for Pieter. Now Pieter has “a certain distance from the labour market.” But has Pieter drifted away from the labour market, or has the labour market drifted away from Pieter? We treat efficiency as though it were a law of nature. As though it is self-evident that work disappears the moment a machine can do it more cheaply. But that is a choice. And every choice has winners and losers. ...

Anyone interested in AI ethics should pay close attention to what is unfolding in America right now. The Department of Defence (given the pointed new title “Department of War” by Trump) is demanding that Anthropic (maker of Claude) lift all usage restrictions on its AI models. Specifically at issue are two safeguards Anthropic had written into its contract: a prohibition on mass surveillance of American citizens, and a prohibition on fully autonomous lethal weapons that select and kill a target without a human in the loop. ...

The profession of software developer is not going to disappear — it is going to become richer than ever. As a programmer you conceive something — a website, a game, an algorithm — and the computer executes it. That is the essence of the craft: translating a human intention into a working computer system. That does not change. But the toolbox has exploded over the past few years. Andrej Karpathy (former AI director at Tesla, co-founder of OpenAI, and the person who coined the term “vibe coding”) has a clear framework for this. He distinguishes three kinds of software: ...

The new Dutch Coalition Agreement pays considerable attention to AI. Rightly so, I think, because AI is going to have a major impact on our lives. This, to my mind, is the key sentence: “We are building a digitally resilient society through an ecosystem approach, working together with education, business and government to take a step forward so that everyone can fully benefit from the opportunities of artificial intelligence.” Good idea! But how do you do that? How do you ensure that “everyone can benefit from the opportunities”? How do you make sure that deploying AI strengthens human autonomy? That AI contributes to a good life? How do we know who benefits, and who bears the risks? ...

Throughout my career I have regularly encountered one of the more elusive elements of software development: estimating how long it takes to build a given piece of functionality. It is a kind of dark art in which intuition, experience and unfounded optimism combine to produce a wild guess at “how long this will take to build.” Among programmers it is a rich source of humour, usually with a kernel of truth. There is, for instance, the “pi rule”: take your estimate and multiply it by pi (roughly 3). ...

Exponential growth: deceptively slow at first, then alarmingly fast. Place one grain of rice on the first square of a chessboard, two on the second, four on the third, eight on the fourth, and so on. How much rice do you need in total for all 64 squares? Take a moment to imagine it. What does your gut say? Now the answer: you need 370 billion tonnes of rice — roughly 670 times current global production. ...